A CLASSIFICATION FRAMEWORK FOR DRUG RELAPSE PREDICTION

Authors

  • A. K. M. Salleh Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, 21300 Gong Badak, Terengganu
  • M. Makhtar Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, 21300 Gong Badak, Terengganu
  • J. A. Jusoh Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, 21300 Gong Badak, Terengganu
  • P. L. Lua Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Universiti Sultan Zainal Abidin, 21300 Gong Badak, Terengganu
  • A. M. Mohamad Faculty of General Studies and Continuing Education, Universiti Sultan Zainal Abidin, 21300 Gong Badak, Terengganu

DOI:

https://doi.org/10.4314/jfas.v9i6s.55

Keywords:

classification, artificial neural network, drug addiction, Inabah rehabilitation

Abstract

This paper proposes a framework for relapse prediction using Artificial Neural Network algorithms among drug addicts at Pusat Rawatan Inabah. The data collected will be mining through Artificial Neural Network algorithms to generate patterns and useful knowledge and then automatically classifying the relapse possibility. This research collaborates with Pusat Rawatan Inabah, which is one of the rehabilitation centers that provide a specific treatment to rehabilitate the drug addicts from addiction. We expect that among the classification data mining algorithms, Artificial Intelligence Neural Network (ANN) is one of the best algorithms to predict relapse among drug addicts. This may help the rehabilitation center to predict relapse individually and the prediction result is hoped to prevent drug addicts from relapse.

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Published

2017-11-10

Issue

Section

Research Articles